Prioritizing human-centered cancer care in a digital era
Bibliographic record
Abstract
Digital health tools improve the efficiency and quality of cancer care and are poised to have an even greater impact in the future. However, the extent to which these tools will enhance both disease-centered and human-centered care depends on which values, outcomes, and processes diverse stakeholders and sectors prioritize. Human-centered care recognizes the uniqueness and inherent value of individuals and values the inimitability of human relationships. In this Viewpoint, we call for prioritization of human-centered care in the design and implementation of digital health tools. After summarizing key ethical frameworks, we provide examples of digital innovations from Brazil, India, and the United States that demonstrate how choices in design, implementation, and evaluation can enhance human-centered care provision. In addition, we provide recommendations to support clinicians, researchers, and health systems in prioritizing human-centered care, including the involvement of patients, caregivers, and communities in all phases of design and implementation. Funding: No funding was used in the creation of this manuscript. WER, ASE, and JEN are partially supported by the NIH/National Cancer Institute comprehensive cancer center award P30 CA008748. WER is supported by the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".